



import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_lowerCamelCase    :		Tuple		      =    os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_lowerCamelCase    :		List[str]		      =    '    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'


class 						lowercase    (							unittest.TestCase):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				tempfile.mkdtemp()
											os.makedirs(os.path.join(self.diffusers_dir       ,	'schedulers/'	)	)
											SCREAMING_SNAKE_CASE							:     Dict						=				self.diffusers_dir
											shutil.copy(
											    os.path.join(UpperCamelCase__       ,	'src/diffusers/schedulers/scheduling_ddpm.py'	)       ,	os.path.join(self.diffusers_dir       ,	'schedulers/scheduling_ddpm.py'	)       ,	)







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				'src/diffusers'
											shutil.rmtree(self.diffusers_dir	)







						def       lowerCamelCase_				(  self    :				Tuple       ,	snake_case    :				Dict       ,	snake_case    :				List[Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				List[str]=None	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
											if overwrite_result is not None:
																SCREAMING_SNAKE_CASE							:     Dict						=				comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
											SCREAMING_SNAKE_CASE							:     str						=				black.Mode(target_versions={black.TargetVersion.PYaa}       ,	line_length=119	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				black.format_str(UpperCamelCase__       ,	mode=UpperCamelCase__	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				os.path.join(self.diffusers_dir       ,	'new_code.py'	)
											with open(UpperCamelCase__       ,	'w'       ,	newline='\n'	) as f:
																f.write(UpperCamelCase__	)
											if overwrite_result is None:
																self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase__	)	) == 0	)
											else:
																check_copies.is_copy_consistent(f.name       ,	overwrite=UpperCamelCase__	)
																with open(UpperCamelCase__       ,	'r'	) as f:
																					self.assertTrue(f.read()       ,	UpperCamelCase__	)







						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'	)
											self.assertEqual(UpperCamelCase__       ,	UpperCamelCase__	)







						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											self.check_copy_consistency(
											    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'       ,	'DDPMSchedulerOutput'       ,	REFERENCE_CODE + '\n'       ,	)

											# With no empty line at the end
											self.check_copy_consistency(
											    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'       ,	'DDPMSchedulerOutput'       ,	UpperCamelCase__       ,	)

											# Copy consistency with rename
											self.check_copy_consistency(
											    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'       ,	'TestSchedulerOutput'       ,	re.sub('DDPM'       ,	'Test'       ,	UpperCamelCase__	)       ,	)

											# Copy consistency with a really long name
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
											self.check_copy_consistency(
											    f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'''       ,	f'''{long_class_name}SchedulerOutput'''       ,	re.sub('Bert'       ,	UpperCamelCase__       ,	UpperCamelCase__	)       ,	)

											# Copy consistency with overwrite
											self.check_copy_consistency(
											    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'       ,	'TestSchedulerOutput'       ,	UpperCamelCase__       ,	overwrite_result=re.sub('DDPM'       ,	'Test'       ,	UpperCamelCase__	)       ,	)

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase     :	Dict       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	int       			=  {
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""trocr"""
  a__       			=						["""past_key_values"""]
  a__       			=						{
      """num_attention_heads""": """decoder_attention_heads""",
      """hidden_size""": """d_model""",
      """num_hidden_layers""": """decoder_layers""",
  }
  def __init__(					self     :					Dict     ,				UpperCamelCase__     :					Optional[Any]=5_0265     ,				UpperCamelCase__     :					int=1024     ,				UpperCamelCase__     :					int=12     ,				UpperCamelCase__     :					List[Any]=16     ,				UpperCamelCase__     :					Union[str, Any]=4096     ,				UpperCamelCase__     :					List[Any]="gelu"     ,				UpperCamelCase__     :					Optional[int]=512     ,				UpperCamelCase__     :					Optional[Any]=0.1     ,				UpperCamelCase__     :					int=0.0     ,				UpperCamelCase__     :					str=0.0     ,				UpperCamelCase__     :					Optional[int]=2     ,				UpperCamelCase__     :					List[str]=0.02     ,				UpperCamelCase__     :					Dict=0.0     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					str=False     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					Tuple=True     ,				UpperCamelCase__     :					str=1     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					List[str]=2     ,				**UpperCamelCase__     :					Tuple     ,				)     ->      Dict:







    """simple docstring"""






    __magic_name__								=			vocab_size
    __magic_name__								=			d_model
    __magic_name__								=			decoder_layers
    __magic_name__								=			decoder_attention_heads
    __magic_name__								=			decoder_ffn_dim
    __magic_name__								=			activation_function
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			dropout
    __magic_name__								=			attention_dropout
    __magic_name__								=			activation_dropout
    __magic_name__								=			init_std
    __magic_name__								=			decoder_layerdrop
    __magic_name__								=			use_cache
    __magic_name__								=			scale_embedding
    __magic_name__								=			use_learned_position_embeddings
    __magic_name__								=			layernorm_embedding

    super().__init__(
        pad_token_id=UpperCamelCase__     ,				bos_token_id=UpperCamelCase__     ,				eos_token_id=UpperCamelCase__     ,				decoder_start_token_id=UpperCamelCase__     ,				**UpperCamelCase__     ,				)

